• Structural Engineering and Mechanics
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• v.31 no.3
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• pp.333-347
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• 2009

Due to structural complicacy, structural health monitoring for civil engineering needs more accurate and effectual methods of damage identification. This study aims to import multi-source information fusion (MSIF) into structural damage diagnosis to improve the validity of damage detection. Firstly, the essential theory and applied mathematic methods of MSIF are introduced. And then, the structural damage identification method based on multi-mode information fusion is put forward. Later, on the basis of a numerical simulation of a concrete continuous box beam bridge, it is obviously indicated that the improved modal strain energy method based on multi-mode information fusion has nicer sensitivity to structural initial damage and favorable robusticity to noise. Compared with the classical modal strain energy method, this damage identification method needs much less modal information to detect structural initial damage. When the noise intensity is less than or equal to 10%, this method can identify structural initial damage well and truly. In a word, this structural damage identification method based on multi-mode information fusion has better effects of structural damage identification and good practicability to actual structures.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.556-570
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• 2021

The existing video expression recognition methods mainly focus on the spatial feature extraction of video expression images, but tend to ignore the dynamic features of video sequences. To solve this problem, a multi-mode convolution neural network method is proposed to effectively improve the performance of facial expression recognition in video. Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. Furthermore, spatial convolution neural network is used to extract the spatial information features of each static expression image, and the dynamic information feature is extracted from the optical flow information of multiple expression images based on temporal convolution neural network. Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. Finally, the fused features are input into support vector machine to realize the facial expression classification. Experimental results show that the recognition accuracy of the proposed method can reach 64.57% and 60.89%, respectively on RML and Baum-ls datasets. It is better than that of other contrast methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2801-2816
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• 2016

Owing to their low scalability, weak support on big data, insufficient data collaborative analysis and inadequate situational awareness, the traditional methods fail to meet the needs of the security data analysis. This paper proposes visualization methods to fuse the multi-source security data and grasp the network situation. Firstly, data sources are classified at their collection positions, with the objects of security data taken from three different layers. Secondly, the Heatmap is adopted to show host status; the Treemap is used to visualize Netflow logs; and the radial Node-link diagram is employed to express IPS logs. Finally, the Labeled Treemap is invented to make a fusion at data-level and the Time-series features are extracted to fuse data at feature-level. The comparative analyses with the prize-winning works prove this method enjoying substantial advantages for network analysts to facilitate data feature fusion, better understand network security situation with a unified, convenient and accurate mode.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.173-182
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• 2005

In this paper, a target tracking algorithm for tracking maneuvering vehicles is presented. The overall algorithm belongs to the category of an interacting multiple-model (IMM) algorithm used to detect multiple targets using fused information from multiple sensors. First, two kinematic models are derived: a constant velocity model for linear motions, and a constant-speed turn model for curvilinear motions. Fpr the constant-speed turn model, a nonlinear information filter is used in place of the extended Kalman filter. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear information filter. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information sharing principle of the federated information filter are discussed. The performance of the suggested algorithm using a Monte Carlo simulation under the two patterns is evaluated.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.5
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• pp.557-564
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• 2012

This paper presents UGV localization using multi-sensor fusion based on federated filter in outdoor environments. The conventional GPS/INS integrated system does not guarantee the robustness of localization because GPS is vulnerable to external disturbances. In many environments, however, vision system is very efficient because there are many features compared to the open space and these features can provide much information for UGV localization. Thus, this paper uses the scene matching and pose estimation based vision navigation, magnetic compass and odometer to cope with the GPS-denied environments. NR-mode federated filter is used for system safety. The experiment results with a predefined path demonstrate enhancement of the robustness and accuracy of localization in outdoor environments.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.67-77
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• 2004

The recent research has been focused on fusion of audio and visual features for reliable speech recognition in noisy environments. In this paper, we propose a neural network based model of robust speech recognition by integrating audio, visual, and contextual information. Bimodal Neural Network(BMNN) is a multi-layer perception of 4 layers, each of which performs a certain level of abstraction of input features. In BMNN the third layer combines audio md visual features of speech to compensate loss of audio information caused by noise. In order to improve the accuracy of speech recognition in noisy environments, we also propose a post-processing based on contextual information which are sequential patterns of words spoken by a user. Our experimental results show that our model outperforms any single mode models. Particularly, when we use the contextual information, we can obtain over 90% recognition accuracy even in noisy environments, which is a significant improvement compared with the state of art in speech recognition. Our research demonstrates that diverse sources of information need to be integrated to improve the accuracy of speech recognition particularly in noisy environments.

• Journal of Positioning, Navigation, and Timing
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• v.1 no.1
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• pp.35-43
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• 2012

The UGV is one of the special field robot developed for mine detection, surveillance and transportation. To achieve successfully the missions of the UGV, the accurate and reliable navigation data should be provided. This paper presents design and implementation of multi-sensor-based open architecture integrated navigation for localization of UGV. The presented architecture hierarchically classifies the integrated system into four layers and data communications between layers are based on the distributed object oriented middleware. The navigation manager determines the navigation mode with the QoS information of each navigation sensor and the integrated filter performs the navigation mode-based data fusion in the filtering process. Also, all navigation variables including the filter parameters and QoS of navigation data can be modified in GUI and consequently, the user can operate the integrated navigation system more usefully. The conventional GPS/INS integrated system does not guarantee the long-term reliability of localization when GPS solution is not available by signal blockage and intentional jamming in outdoor environment. The presented integration algorithm, however, based on the adaptive federated filter structure with FDI algorithm can integrate effectively the output of multi-sensor such as 3D LADAR, vision, odometer, magnetic compass and zero velocity to enhance the accuracy of localization result in the case that GPS is unavailable. The field test was carried out with the UGV and the test results show that the presented integrated navigation system can provide more robust and accurate localization performance than the conventional GPS/INS integrated system in outdoor environments.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.92-101
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• 2018

As the interactive AI speaker becomes popular, voice recognition is regarded as an important vehicle-driver interaction method in case of autonomous driving situation. The purpose of this study is to confirm whether multimodal interaction in which feedback is transmitted by auditory and visual mode of AI characters on screen is more effective in user experience optimization than auditory mode only. We performed the interaction tasks for the music selection and adjustment through the AI speaker while driving to the experiment participant and measured the information and system quality, presence, the perceived usefulness and ease of use, and the continuance intention. As a result of analysis, the multimodal effect of visual characters was not shown in most user experience factors, and the effect was not shown in the intention of continuous use. Rather, it was found that auditory single mode was more effective than multimodal in information quality factor. In the semi-autonomous driving stage, which requires driver 's cognitive effort, multimodal interaction is not effective in optimizing user experience as compared to single mode interaction.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.4
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• pp.39-46
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• 2018

Two surfaces that have relative motion show different characteristics according to surface roughness or surface patterns in all lubrication areas. For two rough surfaces with mixed lubrication, this paper proposes a new approach that includes the contact characteristics of the surfaces and a probabilistic method for a numerical analysis of lubrication. As the contact area of the two surfaces changes according to the loading conditions, asperity contact is very important. An average flow model developed by Patir-Cheng is central to the study of lubrication for rough surfaces. This average flow model also refers to a multi-asperity contact model for deriving a modified Reynolds equation and calculating the lubricant characteristics of a bearing surface with random roughness during fluid flow. Based on the average flow model, this paper carried out a numerical analysis of lubrication using a contact model by considering a load change made by the actual contact of asperities between two surfaces. Lubrication properties show different characteristics according to the surface patterns. This study modeled various geometric surface patterns and calculated the characteristics of lubrication.

• Korean Security Journal
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• no.31
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• pp.157-185
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• 2012

Today, the emergence of cyberspace and advancement of globalization caused not only the transformation of our productive and conventional life but also the revolutionary transition of use of destructive violence such as crime and warfare. This transition of environmental condition connects various security threats which separatedly existed in individual, local, national, and global levels in the past, and transformed the mechanical sum of all levels of security threats into the organic sum of multi-dimensional security threats. This article proposes that the sum of multi-dimensional security threats is caused by the interconnectivity of various different levels of security threats and the integrated interdisciplinary perspective is essential to properly understand the fundamental existence of today's security problem and the reality of fear that we face today. The holistic security, the concept proposed here, is to suggest the mode of networked response to multi-dimensional security threats. The holistic security is suggested to overcome the conventional divisional approach based on the principle of "division of labor" and bureaucratic principles, which means more concretely that national security and criminal justice are divided and intelligence, military, police, prosecution, fire-fighting, private security, and etc. are strictly separated into its own expertise and turf. Also, this article introduces integrated security approaches tried by international organization and major countries overseas with the respect of the holistic security. The author have spent some substantial experience of participant observation, meetings, seminar, conference, and expert interviews regarding the issues discussed in the article in various countries including the United States, Russia, Austria, Germany, Canada, Mexico, Israel, and Uzbekistan for the last ten years. Intelligence and information on various levels of security threats and security approaches introduced in this paper is obtained from such opportunities.